DEGENERALIZING INSTRUMENT APPROACH MINIMA TO NON INSTRUMENT RUNWAYS: A CASE STUDY FOR PAYERNE AIRPORT IN SWITZERLAND

Instrument approaches to non- instrument runways were made possible by the new approach classification of ICAO. As a conservative solution, the procedure design guidelines by the national civil aviation authorities apply circling minima to those approaches to non-instrument runways. However, the classification as non-instrument runway is very binary. Often a small item causes a reduction from instrument to a non-instrument runway and the circling minima become thus very conservative. Here, two cases are shown, Payerne, a non-instrument runway, and Ouessant with an instrument runway, both equipped very differently but both serving Instrument Flight Rules (IFR) traffic. Solutions for Payerne and other similarly highly equipped non-instrument runways are proposed in order to be able to accommodate at least non-precision minima.

A Least-Loss Algorithm for a Bi-Objective One-Dimensional Cutting-Stock Problem

This article presents a new model and an efficient solution algorithm for a bi-objective one-dimensional cutting-stock problem. In the cutting-stock—or trim-loss—problem, customer orders of different smaller item sizes are satisfied by cutting a number of larger standard-size objects. After cutting larger objects to satisfy orders for smaller items, the remaining parts are considered as useless or wasted material, which is called “trim-loss.” The two objectives of the proposed model, in the order of priority, are to minimize the total trim loss, and the number of partially cut large objects. To produce near-optimum solutions, a two-stage least-loss algorithm (LLA) is used to determine the combinations of small item sizes that minimize the trim loss quantity. Solving a real-life industrial problem as well as several benchmark problems from the literature, the algorithm demonstrated considerable effectiveness in terms of both objectives, in addition to high computational efficiency.

SOFT-ASSIGNMENT RANDOM-FOREST WITH AN APPLICATION TO DISCRIMINATIVE REPRESENTATION OF HUMAN ACTIONS IN VIDEOS

The bag-of-features model is a distinctive and robust approach to detect human actions in videos. The discriminative power of this model relies heavily on the quantization of the video features into visual words. The quantization determines how well the visual words describe the human action. Random forests have proven to efficiently transform the features into distinctive visual words. A major disadvantage of the random forest is that it makes binary decisions on the feature values, and thus not taking into account uncertainties of the values. We propose a soft-assignment random forest, which is a generalization of the random forest, by substitution of the binary decisions inside the tree nodes by a sigmoid function. The slope of the sigmoid models the degree of uncertainty about a feature's value. The results demonstrate that the soft-assignment random forest improves significantly the action detection accuracy compared to the original random forest. The human actions that are hard to detect — because they involve interactions with or manipulations of some (typically small) item — are structurally improved. Most prominent improvements are reported for a person handing, throwing, dropping, hauling, taking, closing or opening some item. Improvements are achieved for the state-of-the-art on the IXMAS and UT-Interaction datasets by using the soft-assignment random forest.